Embodiments of the present invention relate to diagnostics, and more specifically to techniques for using an aggregate model for gathering evidence for performing diagnostics for a monitored system.
Various conventional techniques exist in the industry today to perform problem diagnosis. These various techniques may range from expert systems at one end of the spectrum for diagnosability to tribal knowledge forums and self-service knowledge bases at the other end. Expert systems have not been successful in establishing themselves as the de facto tool for problem diagnosis. For example, the expert systems are rule-based and deterministic. It may be a challenging task to determine a set of rules to correctly identify problems in a generic environment that applies to all user systems. Further, full failure data may not be readily available for problem diagnosis, resulting more often than not in failed rule assertions. With only partially captured failure data, it is often difficult to identify an appropriate set of rules that can correctly identify the cause of a problem.
These problems are also not solved by knowledge forums and self-service knowledge bases. The ineffectiveness of expert systems for problem diagnosis gives rise to problem diagnostic solutions using knowledge bases for user self-services or to online forums for community helps. These solutions are based on expressing tribal knowledge in an unstructured form via text or discontinued discourse threads that may require users to tediously read, understand, and interpret the often-incomplete tribal knowledge into corrective actions. Finding the right textual documents that describe the problem is often difficult and time-consuming. Accordingly, problem diagnosis based on tribal knowledge that is expressed in unstructured forms via a knowledge base and discontinued discourse in online forums may result in incomplete or unclear problem signature and characterization. Further, these approaches may require certain subjective interpretation from users, resulting in incorrect problem isolation and identification that leads to false faults.